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<table width="100%"><tr><td>predict.plsone(plstools)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
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<h2>Predict method for Partial Least Squares Fits</h2>


<h3>Description</h3>

<p>
The function 'predict.plsone'retruns predicted values,
confidence and tolerance intervals.
</p>


<h3>Usage</h3>

<pre>
## S3 method for class 'plsone':
predict(object, newdata, se.fit = FALSE,interval="none",level = 0.95,...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
Object of class inheriting from 'plsone'</td></tr>
<tr valign="top"><td><code>newdata</code></td>
<td>
An optional data frame in which to look for variables with which to predict.
If omitted, the fitted values are used.</td></tr>
<tr valign="top"><td><code>se.fit</code></td>
<td>
A switch indicating if standard errors are required.</td></tr>
<tr valign="top"><td><code>interval</code></td>
<td>
none, confidence, prediction</td></tr>
<tr valign="top"><td><code>level</code></td>
<td>
Tolerance/confidence level</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments passed to or from other methods.</td></tr>
</table>

<h3>Details</h3>

<p>
add formula for tolerance and confidence interval
</p>


<h3>Value</h3>

<p>
The function 'predict.plsone'retruns ...
</p>
<table summary="R argblock">
<tr valign="top"><td><code>comp1 </code></td>
<td>
Description of 'comp1'</td></tr>
<tr valign="top"><td><code>comp2 </code></td>
<td>
Description of 'comp2'</td></tr>
</table>
<p>

...</p>

<h3>References</h3>

<p>
Tenenhaus M. (1998) La Regression PLS. Theorie et pratique. Technip, Paris.<br>
</p>


<h3>See Also</h3>

<p>
<code><a href="plsone.html">plsone</a></code>, <code><a onclick="findlink('stats', 'predict.lm.html')" style="text-decoration: underline; color: blue; cursor: hand">predict.lm</a></code>
</p>


<h3>Examples</h3>

<pre>
require(pls)
data(yarn)
yarn.pls &lt;- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
plstest1 &lt;- plsone(density ~ NIR, nf=6, data = yarn,scale=FALSE)
plot(predict(plstest1)$fitted,plstest1$y)
</pre>

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